基于个人意愿的社会网络团体结构与信息检测方案

doi:10.3969/j.issn.0372-2112.2019.04.017

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摘要个人意愿对于形成网络社团和传播信息有着重要的影响力，因此本文提出一种基于个人意愿的社团结构与信息检测方案.该方案中的社团检测算法初次检测以融入节点属性的模块度，再次检测以兴趣度并能发现重叠社团，最后精细检测以个人意愿，本文社团检测算法（ε_CSDA）较之前的算法更有效的是可以发现重叠社团；同时，该方案建立的信息传播模型在指数模型基础上构建边特征向量（边属性）、节点特征向量（节点属性）和意愿向量（用户意愿、社团意愿和节点意愿），并以传播概率和传播延迟构建模型基本关系，从而使得该模型实现了基于个人意愿的信息传播.实验结果表明，加入个人意愿的社团检测和信息传播方案，能够保证社团检测的有效性和实用性，能够实现用户间信息传播的主动性和可靠性.

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	汪林玉
	谷科
	余飞
	尹波
	廖年冬

关键词 ：社会网络, 个人意愿, 社团检测, 重叠社团, 信息传播

Abstract：Personal willingness is one of the most important factors influencing the construction of social community and the information dissemination in social network.In this paper,we propose a social community structure and information detection scheme based on personal willingness in social network.In our proposed scheme,the social community detection algorithm uses the node attributes to detect social community structure and further find overlapping communities;the information dissemination method is based on the exponential model,which constructs the feature vector by the edge feature and the node feature,the willingness vector by the personal willingness and the community willingness,and the basic relationship by the dissemination probability and dissemination delay.Experimental results show that our proposed scheme can ensure the effectiveness of social community detection and the initiative and reliability of information dissemination.

Key words： social network personal willingness community detection overlapping community information dissemination

收稿日期: 2017-03-31 出版日期: 2018-11-27

ZTFLH:

TP393

基金资助:国家自然科学基金（No.61402055，No.61462048，No.61504013）；湖南省自然科学基金（No.2018JJ2445，No.2016JJ3012）；"综合交通运输大数据智能处理"湖南省重点开放基金（No.JTXY16B03，No.JTXY16B01，No.JTXY16B05）

作者简介: 汪林玉 女,1993年5月出生,湖南长沙人.现为湖南信息学院电子信息学院教师.主要研究方向为社会网络和信息安全;谷科男,1980年4月出生,湖南长沙人.博士,现为长沙理工大学计算机与通信工程学院硕士生导师.主要研究方向为网络和信息安全.

引用本文:

汪林玉, 谷科, 余飞, 尹波, 廖年冬. 基于个人意愿的社会网络团体结构与信息检测方案[J]. 电子学报, 2019, 47(4): 886-895. WANG Lin-yu, GU Ke, YU Fei, YIN Bo, LIAO Nian-dong. Social Community Structure and Information Detection Scheme Based on Personal Willingness. Acta Electronica Sinica, 2019, 47(4): 886-895.

链接本文:

http://www.ejournal.org.cn/CN/10.3969/j.issn.0372-2112.2019.04.017 或 http://www.ejournal.org.cn/CN/Y2019/V47/I4/886

[1] 余智欣,黄天戍,等.一种新型的分布式隐私保护计算模型及其应用[J].西安交通大学学报,2007,41(8):954-958. YU Zhi-xin,HUANG Tian-shu,et al.Novel privacy-protecting distributed computation model and its applications[J].Journal of Xi'an Jiaotong University,2007,41(8):954-958.(in Chinese)
[2] SHEN H,CHENG X,CAI K,et al.Detect overlapping and hierarchical community structure in networks[J].Physica A:Statistical Mechanics and Its Applications,2009,388(8):1706-1712.
[3] BLONDEL V D,GUILLAUME J L,et al.Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics:Theory and Experiment,2008,(10):P10-008.
[4] AHN Y Y,BAGROW J P,LEHMANN S.Link communities reveal multiscale complexity in networks[J].Nature,2010,466(7307):761-764.
[5] VON LUXBURG U.A tutorial on spectral clustering[J].Statistics and Computing,2007,17(4):395-416.
[6] WHANG J J,GLEICH D F,DHILLON I S.Overlapping community detection using seed set expansion[A].Proceedings of the 22nd ACM International Conference on Information & Knowledge Management[C].US:ACM,2013.2099-2108.
[7] PONS P,LATAPY M.Computing communities in large networks using random walks[J].Journal of Graph Algorithms and Applications,2006,10(2):191-218.
[8] RAGHAVAN U N,ALBERT R,KUMARA S.Near linear time algorithm to detect community structures in large-scale networks[J].Physical Review E,2007,76(3):036106.
[9] DANG T A,VIENNET E.Community detection based on structural and attribute similarities[A].International Conference on Digital Society[C].US:ICDS,2012.7-12.
[10] KEWALRAMANI M N.Community Detection in Twitter[M].Baltimore County,US:University of Maryland,2011.231-300.
[11] DEITRICK W,HU W.Mutually enhancing community detection and sentiment analysis on twitter networks[J].Journal of Data Analysis & Information Processing,2013,1(3):19-29.
[12] 孙怡帆,李赛.基于相似度的微博社交网络的社区发现方法[J].计算机研究与发展,2014,51(12):2797-2807. SUN Yi-fan,LI Sai.Similarity-based community detection in social network of microblog[J].Journal of Computer Research and Development,2014,51(12):2797-2807.(in Chinese)
[13] GOLDENBERG J,LIBAI B,MULLER E.Talk of the network:A complex systems look at the underlying process of word-of-mouth[J].Marketing Letters,2001,12(3):211-223.
[14] YOUNG H P.The diffusion of innovations in social networks[J].General Information,2000,413(1):2329-2334.
[15] KEMPE D,KLEINBERG J,TARDOS É.Maximizing the spread of influence through a social network[A].ACM SIGKDD International Conference on Knowledge Discovery & Data Mining[C].US:ACM,2003.137-146.
[16] LAGNIER C,DENOYER L,et al.Predicting information diffusion in social networks using content and user's profiles[A].European Conference on Information Retrieval[C].Berlin Heidelberg:Springer,2013.74-85.
[17] SAITO K,OHARA K,et al.Learning diffusion probability based on node attributes in social networks[A].International Symposium on Methodologies for Intelligent Systems[C].Berlin Heidelberg:Springer,2011.153-162.
[18] SPIRO E,IRVINE C,et al.Waiting for a retweet:modeling waiting times in information propagation[A].NIPS Workshop of Social Networks and Social Media Conference[C/OL].http://snap.stanford.edu/social2012/papers/spiro-dubois-butts.pdf.2012,12.
[19] 刘瑶,康晓慧,高红,等.基于节点亲密度和度的社会网络社团发现方法[J].计算机研究与发展,2015,52(10):2363-2372.
[20] 李致远,陈汝龙,王汝传.基于兴趣和行为预测的移动社交网络动态资源发现机制[J].通信学报,2016,37(4):34-43. LI Zhi-yuan,CHEN Ru-long,WANG Ru-chuan.Exploiting interests and behavior prediction for dynamic resource discovery in mobile social networking[J].Journal on Communication,2016,37(4):34-43.(in Chinese)
[21] 周东浩,韩文报,王勇军.基于节点和信息特征的社会网络信息传播模型[J].计算机研究与发展,2015,52(1):156-166. ZHOU Dong-hao,HAN Wen-bao,WANG Yong-jun.A fine-grained information diffusion model based on node attributes and content features[J].Journal of Computer Research and Development,2015,52(1):156-166.(in Chinese)
[22] GOYAL A,BONCHI F,LAKSHMANAN L V S.Learning influence probabilities in social networks[A].International Conference on Web Search and Web Data Mining (WSDM)[C].New York,US:ACM,2010.241-250.
[23] AGARWAL A,XIE B,VOVSHA I,et al.Sentiment analysis of Twitter data[A].The Workshop on Languages in Social Media[C].US:Association for Computational Linguistics,2011.30-38.
[24] GIRVAN M,NEWMANM E J.Community structure in social and biological networks[J].Proceedings of the National Academy of Sciences,2002,99(12):7821-7826.
[25] NEWMAN M E J.Analysis of weighted networks[J].Physical Review E Statistical Nonlinear & Soft Matter Physics,2004,70(5 Pt 2):056131.